




"""simple docstring"""




import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to properly calculate the metrics on the
# validation dataset when in a distributed system, and builds off the
# `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


lowerCAmelCase				:     Tuple					=				16
lowerCAmelCase				:     Optional[Any]					=				32



def        a__					(							snake_case__			,					snake_case__ = 16							)		->   Any:
			lowerCamelCase      =   AutoTokenizer.from_pretrained("""bert-base-cased"""							)
			lowerCamelCase      =   load_dataset("""glue"""			,					"""mrpc"""							)

			def tokenize_function(snake_case__							):
						# max_length=None => use the model max length (it's actually the default)
						lowerCamelCase      =   tokenizer(examples["""sentence1"""]			,					examples["""sentence2"""]			,					truncation=snake_case__			,					max_length=snake_case__							)
						return outputs

			# Apply the method we just defined to all the examples in all the splits of the dataset
			# starting with the main process first:
			with accelerator.main_process_first():
						lowerCamelCase      =   datasets.map(
						    snake_case__			,					batched=snake_case__			,					remove_columns=["""idx""", """sentence1""", """sentence2"""]			,					)

			# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
			# transformers library
			lowerCamelCase      =   tokenized_datasets.rename_column("""label"""			,					"""labels"""							)

			def collate_fn(snake_case__							):
						# On TPU it's best to pad everything to the same length or training will be very slow.
						lowerCamelCase      =   1_28 if accelerator.distributed_type == DistributedType.TPU else None
						# When using mixed precision we want round multiples of 8/16
						if accelerator.mixed_precision == "fp8":
									lowerCamelCase      =   16
						elif accelerator.mixed_precision != "no":
									lowerCamelCase      =   8
						else:
									lowerCamelCase      =   None

						return tokenizer.pad(
						    snake_case__			,					padding="""longest"""			,					max_length=snake_case__			,					pad_to_multiple_of=snake_case__			,					return_tensors="""pt"""			,					)

			# Instantiate dataloaders.
			lowerCamelCase      =   DataLoader(
			    tokenized_datasets["""train"""]			,					shuffle=snake_case__			,					collate_fn=snake_case__			,					batch_size=snake_case__							)
			lowerCamelCase      =   DataLoader(
			    tokenized_datasets["""validation"""]			,					shuffle=snake_case__			,					collate_fn=snake_case__			,					batch_size=snake_case__							)

			return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
					from accelerate.test_utils.training import mocked_dataloaders

					lowerCAmelCase				:     Optional[int]					=				mocked_dataloaders  # noqa: F811



def        a__					(							snake_case__			,					snake_case__							)		->   str:
			# For testing only
			if os.environ.get("""TESTING_MOCKED_DATALOADERS"""			,					snake_case__							) == "1":
						lowerCamelCase      =   2
			# Initialize accelerator
			lowerCamelCase      =   Accelerator(cpu=args.cpu			,					mixed_precision=args.mixed_precision							)
			# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
			lowerCamelCase      =   config["""lr"""]
			lowerCamelCase      =   int(config["""num_epochs"""]							)
			lowerCamelCase      =   int(config["""seed"""]							)
			lowerCamelCase      =   int(config["""batch_size"""]							)

			lowerCamelCase      =   evaluate.load("""glue"""			,					"""mrpc"""							)

			# If the batch size is too big we use gradient accumulation
			lowerCamelCase      =   1
			if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
						lowerCamelCase      =   batch_size // MAX_GPU_BATCH_SIZE
						lowerCamelCase      =   MAX_GPU_BATCH_SIZE

			set_seed(snake_case__							)
			lowerCamelCase ,  lowerCamelCase      =   get_dataloaders(snake_case__			,					snake_case__							)
			# Instantiate the model (we build the model here so that the seed also control new weights initialization)
			lowerCamelCase      =   AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""			,					return_dict=snake_case__							)

			# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
			# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
			# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
			lowerCamelCase      =   model.to(accelerator.device							)

			# Instantiate optimizer
			lowerCamelCase      =   AdamW(params=model.parameters()			,					lr=snake_case__							)

			# Instantiate scheduler
			lowerCamelCase      =   get_linear_schedule_with_warmup(
			    optimizer=snake_case__			,					num_warmup_steps=1_00			,					num_training_steps=(len(snake_case__							) * num_epochs) // gradient_accumulation_steps			,					)

			# Prepare everything
			# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
			# prepare method.
			lowerCamelCase ,  lowerCamelCase ,  lowerCamelCase ,  lowerCamelCase ,  lowerCamelCase      =   accelerator.prepare(
			    snake_case__			,					snake_case__			,					snake_case__			,					snake_case__			,					snake_case__							)

			# Now we train the model
			for epoch in range(snake_case__							):
						model.train()
						for step, batch in enumerate(snake_case__							):
									# We could avoid this line since we set the accelerator with `device_placement=True`.
									batch.to(accelerator.device							)
									lowerCamelCase      =   model(**snake_case__							)
									lowerCamelCase      =   outputs.loss
									lowerCamelCase      =   loss / gradient_accumulation_steps
									accelerator.backward(snake_case__							)
									if step % gradient_accumulation_steps == 0:
												optimizer.step()
												lr_scheduler.step()
												optimizer.zero_grad()

						model.eval()
						lowerCamelCase      =   0
						for step, batch in enumerate(snake_case__							):
									# We could avoid this line since we set the accelerator with `device_placement=True`.
									batch.to(accelerator.device							)
									with torch.no_grad():
												lowerCamelCase      =   model(**snake_case__							)
									lowerCamelCase      =   outputs.logits.argmax(dim=-1							)
									lowerCamelCase ,  lowerCamelCase      =   accelerator.gather((predictions, batch["""labels"""])							)
									# New Code #
									# First we check if it's a distributed system
									if accelerator.use_distributed:
												# Then see if we're on the last batch of our eval dataloader
												if step == len(snake_case__							) - 1:
															# Last batch needs to be truncated on distributed systems as it contains additional samples
															lowerCamelCase      =   predictions[: len(eval_dataloader.dataset							) - samples_seen]
															lowerCamelCase      =   references[: len(eval_dataloader.dataset							) - samples_seen]
												else:
															# Otherwise we add the number of samples seen
															samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
									metric.add_batch(
									    predictions=snake_case__			,					references=snake_case__			,					)

						lowerCamelCase      =   metric.compute()
						# Use accelerator.print to print only on the main process.
						accelerator.print(F'epoch {epoch}:'			,					snake_case__							)



def        a__					(							)		->   Union[str, Any]:
			lowerCamelCase      =   argparse.ArgumentParser(description="""Simple example of training script."""							)
			parser.add_argument(
			    """--mixed_precision"""			,					type=snake_case__			,					default=snake_case__			,					choices=["""no""", """fp16""", """bf16""", """fp8"""]			,					help="""Whether to use mixed precision. Choose"""
			    """between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
			    """and an Nvidia Ampere GPU."""			,					)
			parser.add_argument("""--cpu"""			,					action="""store_true"""			,					help="""If passed, will train on the CPU."""							)
			lowerCamelCase      =   parser.parse_args()
			lowerCamelCase      =   {"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
			training_function(snake_case__			,					snake_case__							)


if __name__ == "__main__":
					main()









"""simple docstring"""




import collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase				:     Any					=				logging.get_logger(__name__)

lowerCAmelCase				:     int					=				"""▁"""

lowerCAmelCase				:     str					=				{"""vocab_file""": """prophetnet.tokenizer"""}

lowerCAmelCase				:     Union[str, Any]					=				{
    """vocab_file""": {
        """microsoft/xprophetnet-large-wiki100-cased""": (
            """https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer"""
        ),
    }
}

lowerCAmelCase				:     Any					=				{
    """microsoft/xprophetnet-large-wiki100-cased""": {"""do_lower_case""": False},
}

lowerCAmelCase				:     List[Any]					=				{
    """microsoft/xprophetnet-large-wiki100-cased""": 512,
}



def        a__					(							snake_case__							)		->   int:
			lowerCamelCase      =   collections.OrderedDict()
			with open(snake_case__			,					"""r"""			,					encoding="""utf-8"""							) as reader:
						lowerCamelCase      =   reader.readlines()
			for index, token in enumerate(snake_case__							):
						lowerCamelCase      =   token.rstrip("""\n"""							)
						lowerCamelCase      =   index
			return vocab






class        __magic_name__				(			UpperCAmelCase__							):






				'''simple docstring'''
				__UpperCamelCase         =		VOCAB_FILES_NAMES
				__UpperCamelCase         =		PRETRAINED_VOCAB_FILES_MAP
				__UpperCamelCase         =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__UpperCamelCase         =		["input_ids", "attention_mask"]





				def __init__(			self						,					_a						,					_a="[SEP]"						,					_a="[SEP]"						,					_a="[SEP]"						,					_a="[UNK]"						,					_a="[PAD]"						,					_a="[CLS]"						,					_a="[MASK]"						,					_a = None						,					**_a						,					):





							"""simple docstring"""
							lowerCamelCase      =   {} if sp_model_kwargs is None else sp_model_kwargs

							super().__init__(
							    bos_token=_a						,					eos_token=_a						,					sep_token=_a						,					unk_token=_a						,					pad_token=_a						,					cls_token=_a						,					mask_token=_a						,					sp_model_kwargs=self.sp_model_kwargs						,					**_a						,					)

							try:
										import sentencepiece as spm
							except ImportError:
										logger.warning(
										    """You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece"""
										    """ pip install sentencepiece"""					)
										raise

							lowerCamelCase      =   spm.SentencePieceProcessor(**self.sp_model_kwargs					)
							self.sp_model.Load(str(_a					)					)
							lowerCamelCase      =   vocab_file

							# Original fairseq vocab and spm vocab must be "aligned":
							# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
							# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
							# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
							# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

							# put special tokens and [unused] tokens into the vocab
							lowerCamelCase      =   {"""[PAD]""": 0, """[CLS]""": 1, """[SEP]""": 2, """[UNK]""": 3, """[MASK]""": 4}

							for i in range(10					):
										lowerCamelCase      =   f'[unused{i}]'
										lowerCamelCase      =   5 + i

							# The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
							lowerCamelCase      =   12
							lowerCamelCase      =   {v: k for k, v in self.fairseq_tokens_to_ids.items()}
							for k in self.fairseq_tokens_to_ids.keys():
										self.unique_no_split_tokens.append(_a					)





				def __getstate__(			self					):





							"""simple docstring"""
							lowerCamelCase      =   self.__dict__.copy()
							lowerCamelCase      =   None
							return state





				def __setstate__(			self						,					_a					):





							"""simple docstring"""
							lowerCamelCase      =   d
							try:
										import sentencepiece as spm
							except ImportError:
										logger.warning(
										    """You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece"""
										    """ pip install sentencepiece"""					)
										raise

							# for backward compatibility
							if not hasattr(self						,					"""sp_model_kwargs"""					):
										lowerCamelCase      =   {}

							lowerCamelCase      =   spm.SentencePieceProcessor(**self.sp_model_kwargs					)
							self.sp_model.Load(self.vocab_file					)





				def   _lowerCAmelCase							(			self						,					_a						,					_a = None						,					_a = False					):





							"""simple docstring"""

							if already_has_special_tokens:
										return super().get_special_tokens_mask(
										    token_ids_a=_a						,					token_ids_a=_a						,					already_has_special_tokens=_a					)

							if token_ids_a is None:
										return ([0] * len(_a					)) + [1]
							return ([0] * len(_a					)) + [1] + ([0] * len(_a					)) + [1]





				def   _lowerCAmelCase							(			self						,					_a						,					_a = None					):





							"""simple docstring"""
							lowerCamelCase      =   [self.sep_token_id]

							if token_ids_a is None:
										return len(token_ids_a + sep					) * [0]
							return len(token_ids_a + sep + sep + token_ids_a + sep					) * [0]





				@property
				def   _lowerCAmelCase							(			self					):





							"""simple docstring"""
							return len(self.sp_model					) + self.fairseq_offset





				def   _lowerCAmelCase							(			self					):





							"""simple docstring"""
							lowerCamelCase      =   {self.convert_ids_to_tokens(_a					): i for i in range(self.vocab_size					)}
							vocab.update(self.added_tokens_encoder					)
							return vocab





				def   _lowerCAmelCase							(			self						,					_a					):





							"""simple docstring"""
							return self.sp_model.encode(_a						,					out_type=_a					)





				def   _lowerCAmelCase							(			self						,					_a					):





							"""simple docstring"""
							if token in self.fairseq_tokens_to_ids:
										return self.fairseq_tokens_to_ids[token]
							lowerCamelCase      =   self.sp_model.PieceToId(_a					)

							# Need to return unknown token if the SP model returned 0
							return spm_id + self.fairseq_offset if spm_id else self.unk_token_id





				def   _lowerCAmelCase							(			self						,					_a					):





							"""simple docstring"""
							if index in self.fairseq_ids_to_tokens:
										return self.fairseq_ids_to_tokens[index]
							return self.sp_model.IdToPiece(index - self.fairseq_offset					)





				def   _lowerCAmelCase							(			self						,					_a					):





							"""simple docstring"""
							lowerCamelCase      =   """""".join(_a					).replace(_a						,					""" """					).strip()
							return out_string





				def   _lowerCAmelCase							(			self						,					_a						,					_a = None					):





							"""simple docstring"""
							if not os.path.isdir(_a					):
										logger.error(f'Vocabulary path ({save_directory}) should be a directory'					)
										return
							lowerCamelCase      =   os.path.join(
							    _a						,					(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]					)

							if os.path.abspath(self.vocab_file					) != os.path.abspath(_a					) and os.path.isfile(self.vocab_file					):
										copyfile(self.vocab_file						,					_a					)
							elif not os.path.isfile(self.vocab_file					):
										with open(_a						,					"""wb"""					) as fi:
													lowerCamelCase      =   self.sp_model.serialized_model_proto()
													fi.write(_a					)

							return (out_vocab_file,)





				def   _lowerCAmelCase							(			self						,					_a						,					_a = None					):





							"""simple docstring"""

							if token_ids_a is None:
										return token_ids_a + [self.sep_token_id]
							lowerCamelCase      =   [self.sep_token_id]
							return token_ids_a + sep + token_ids_a + sep



